Digital image processing is currently used in a variety of applications. Digital image processing includes image data acquisition, restoration, encoding, decoding processing, object recognition, and data filtering and enhancement. The image data that is processed during digital image processing includes, but is not limited to, motion pictures and images. During processing, the image data may become distorted due to introduction of artifacts in the image data. The artifacts are distortions in the image data that can be introduced inadvertently by hardware or software, or unintentionally by an operator. Artifacts introduced by hardware and software generally degrade the image data and distort interpretation. The artifacts may also be introduced in the image data due to faults that occur during acquisition of the image data, such as the improper handling of an image data acquisition device, or an induced noise. The artifacts introduced in the image data include, but are not limited to, an interlace artifact, a blur artifact, an aliasing artifact, and a noise artifact. The interlace artifact is introduced during interlacing of the motion picture. Interlacing involves creating frames of the motion picture using multiple fields. Interlaced motion picture is designed to be captured, transmitted or stored and displayed in the same interlaced format. The fields are captured at different instances; hence the interlaced motion picture exhibits motion artifacts if the recorded objects are moving fast enough to be in different positions when each individual field is captured. These artifacts may be visible when interlaced motion picture is displayed at a slower speed than it was captured or when still frames are presented. The presence of the artifacts results in an incorrect perception of the image data by the human eye. To overcome the artifacts, their calculation is necessary.
A traditional technique for calculating artifacts in the image data calculates the interlace artifact by using vertical frequency detection. Frequencies in the image data are detected by using the Discrete Fourier Transform that are then used to calculate the interlace artifact. However, the aforementioned technique fails to quantify and estimate the quality of the motion picture. Another traditional technique calculates the motion between frames of the motion picture. The motion between the frames is then used to calculate the interlace artifact. However, this technique suffers from the drawback of being incapable of quantifying the quality parameters of the motion picture.
Yet another traditional technique detects the motion of focused objects in an interlaced motion picture by using motion vectors. However, this technique does not quantify the artifacts.
Therefore, there exists a need for a method and a system for calculating the interlace artifact in the image data. The method and the system should preferably be capable of real-time quality checking of the image data. Further, the method and the system for calculating the interlace artifact in the image data should preferably be computationally faster than the traditional techniques for calculating the interlace artifact.